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Working with Multiple DataFrames
Outer Merge

In the previous exercise, we saw that when we merge two DataFrames whose rows don’t match perfectly, we lose the unmatched rows.

This type of merge (where we only include matching rows) is called an inner merge. There are other types of merges that we can use when we want to keep information from the unmatched rows.

Suppose that two companies, Company A and Company B have just merged. They each have a list of customers, but they keep slightly different data. Company A has each customer’s name and email. Company B has each customer’s name and phone number. They have some customers in common, but some are different.

`company_a`

name email
Sally Sparrow sally.sparrow@gmail.com
Peter Grant pgrant@yahoo.com
Leslie May leslie_may@gmail.com

`company_b`

name phone
Peter Grant 212-345-6789
Leslie May 626-987-6543
Aaron Burr 303-456-7891

If we wanted to combine the data from both companies without losing the customers who are missing from one of the tables, we could use an Outer Join. An Outer Join would include all rows from both tables, even if they don’t match. Any missing values are filled in with `None` or `nan` (which stands for “Not a Number”).

``pd.merge(company_a, company_b, how='outer')``

The resulting table would look like this:

name email phone
Sally Sparrow sally.sparrow@gmail.com `nan`
Peter Grant pgrant@yahoo.com 212-345-6789
Leslie May leslie_may@gmail.com 626-987-6543
Aaron Burr `nan` 303-456-7891

### Instructions

1.

There are two hardware stores in town: Store A and Store B. Store A’s inventory is in DataFrame `store_a` and Store B’s inventory is in DataFrame `store_b`. They have decided to merge into one big Super Store!

Combine the inventories of Store A and Store B using an outer merge. Save the results to the variable `store_a_b_outer`.

2.

Display `store_a_b_outer` using `print`.

Which values are `nan` or `None`?

What does that mean?